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Enhancing remote sensing image retrieval using a triplet deep metric learning network
| Content Provider | Scilit |
|---|---|
| Author | Cao, Rui Zhang, Qian Zhu, Jiasong Li, Qing Li, Qingquan Liu, Bozhi Qiu, Guoping |
| Copyright Year | 2019 |
| Description | With the rapid growing of remotely sensed imagery data, there is a high demand for effective and efficient image retrieval tools to manage and exploit such data. In this letter, we present a novel content-based remote sensing image retrieval (RSIR) method based on Triplet deep metric learning convolutional neural network (CNN). By constructing a Triplet network with metric learning objective function, we extract the representative features of the images in a semantic space in which images from the same class are close to each other while those from different classes are far apart. In such a semantic space, simple metric measures such as Euclidean distance can be used directly to compare the similarity of images and effectively retrieve images of the same class. We also investigate a supervised and an unsupervised learning methods for reducing the dimensionality of the learned semantic features. We present comprehensive experimental results on two public RSIR datasets and show that our method significantly outperforms state-of-the-art. |
| Related Links | http://arxiv.org/pdf/1902.05818 |
| Ending Page | 751 |
| Page Count | 12 |
| Starting Page | 740 |
| ISSN | 01431161 |
| e-ISSN | 13665901 |
| DOI | 10.1080/2150704x.2019.1647368 |
| Journal | International Journal of Remote Sensing |
| Issue Number | 2 |
| Volume Number | 41 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-01-17 |
| Access Restriction | Open |
| Subject Keyword | Journal: International Journal of Remote Sensing Urban and Regional Planning Image Retrieval Neural Deep Metric Function Rsir Semantic Triplet Images |
| Content Type | Text |
| Resource Type | Article |
| Subject | Earth and Planetary Sciences |
